Flipboard

SEPTEMBER 4, 2023

Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. This is suitable for making a variety of Python applications with other dependencies being added to it at the user’s convenience.

Machine Learning 109

Machine Learning Computer Vision Deep Learning Python 109

The MLOps Blog

MAY 10, 2023

How to save a trained model in Python? In this section, you will see different ways of saving machine learning (ML) as well as deep learning (DL) models. The first way to save an ML model is by using the pickle file. Saving trained model with pickle The pickle module can be used to serialize and deserialize the Python objects.

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Python Metadata ML Deep Learning 106

Flipboard

OCTOBER 9, 2023

Getting Used to Docker for Machine Learning Introduction Docker is a powerful addition to any development environment, and this especially rings true for ML Engineers or enthusiasts who want to get started with experimentation without having to go through the hassle of setting up several drivers, packages, and more.

Machine Learning 116

Machine Learning Computer Vision Deep Learning Python 116

Viso.ai

FEBRUARY 27, 2024

Since its inception in 2015, the YOLO (You Only Look Once) object-detection algorithm has been closely followed by tech enthusiasts, data scientists, ML engineers, and more, gaining a massive following due to its open-source nature and community contributions. YOLOv5 uses YAML files instead of CFG files in the model configurations.

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Neural Network Computer Vision Convolutional Neural Networks Python 52

AWS Machine Learning Blog

SEPTEMBER 29, 2023

Planet and AWS’s partnership on geospatial ML SageMaker geospatial capabilities empower data scientists and ML engineers to build, train, and deploy models using geospatial data. This example uses the Python client to identify and download imagery needed for the analysis.

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Machine Learning ML Data Scientist Python 112

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

AWS Machine Learning Blog

APRIL 25, 2023

With this new capability of the SageMaker Python SDK, data scientists can onboard their ML code to the SageMaker Training platform in a few minutes. In this release, you can run your local machine learning (ML) Python code as a single-node Amazon SageMaker training job or multiple parallel jobs.

Machine Learning 78

Machine Learning Data Scientist Python ML 78

AWS Machine Learning Blog

NOVEMBER 30, 2023

This container image has all the most popular ML frameworks supported by SageMaker, along with SageMaker Python SDK , boto3 , and other AWS and data science specific libraries installed. In this example, Code Editor can be used by an ML engineering team who needs advanced IDE features to debug their code and deploy the endpoint.

Machine Learning 102

Machine Learning ML LLM Software Engineer 102

AWS Machine Learning Blog

JUNE 27, 2023

Earth.com’s leadership team recognized the vast potential of EarthSnap and set out to create an application that utilizes the latest deep learning (DL) architectures for computer vision (CV). We initiated a series of enhancements to deliver managed MLOps platform and augment ML engineering.

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DevOps ML Machine Learning Automation 94

AWS Machine Learning Blog

OCTOBER 2, 2023

In this series, we walk you through the process of architecting and building an integrated end-to-end MLOps pipeline for a computer vision use case at the edge using SageMaker, AWS IoT Greengrass, and the AWS Cloud Development Kit (AWS CDK). The following diagram illustrates what this could look like for our computer vision pipeline.

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DevOps Automation ML Computer Vision 91

Pickl AI

JANUARY 23, 2023

It supports languages like Python and R and processes the data with the help of data flow graphs. It is an open-source framework that is written in Python and can efficiently operate on both GPUs and CPUs. Keras supports a high-level neural network API written in Python. Cons Low level computation cannot be handled by keras.

Machine Learning 52

Machine Learning ML Neural Network Auto-classification 52

AWS Machine Learning Blog

NOVEMBER 20, 2023

KT’s AI Food Tag is an AI-based dietary management solution that identifies the type and nutritional content of food in photos using a computer vision model. region_name}.amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker' amazonaws.com/pytorch-training:2.0.0-gpu-py310-cu118-ubuntu20.04-sagemaker'

Deep Learning 91

Deep Learning Machine Learning ML Data Scientist 91

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Overall, Ravi aims to provide insights into how computer vision data and models can be effectively improved in tandem and adjusted for downstream applications.

Data Scientist 64

Data Scientist AI AI ML 64

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. Overall, Ravi aims to provide insights into how computer vision data and models can be effectively improved in tandem and adjusted for downstream applications.

Data Scientist 59

Data Scientist AI AI ML 59

AWS Machine Learning Blog

JANUARY 25, 2023

Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers to explain the predictions of their ML models. We use the SageMaker Python SDK for this purpose. He focuses on Deep learning including NLP and Computer Vision domains.

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Explainability Natural Language Processing Algorithm Machine Learning 70

The MLOps Blog

OCTOBER 3, 2023

How did you manage to jump from a more analytical, scientific type of role to a more engineering one? Mikiko Bazeley: Most people are really surprised to hear that my background in college was not computer science. I actually did not pick up Python until about a year before I made the transition to a data scientist role.

ML 52

ML Data Scientist Machine Learning Data Science 52

AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. He has worked with organizations ranging from large enterprises to mid-sized startups on problems related to distributed computing, and Artificial Intelligence.

Generative AI 65

Generative AI Auto-complete AI Modeling Machine Learning 65

The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc.,

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

AWS Machine Learning Blog

APRIL 19, 2023

Most publicly available fraud detection datasets don’t provide this information, so we use the Python Faker library to generate a set of transactions covering a 5-month period. About the Authors Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build AI/ML solutions. This dataset contains 5.4

ML 77

ML Data Scientist Big Data Machine Learning 77

The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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Machine Learning Data Scientist ML Metadata 69